Best AI Tools for Data analysts in 2026

Why data analysts are adopting AI tools in 2026

Data analysts face pressure to ship faster, reduce manual busywork, and improve output quality without linear headcount growth. AI tools now cover drafting, research, design, analytics, customer conversations, and code — not as experiments but as daily infrastructure. Teams that standardize on a small, integrated stack typically see quicker turnaround on repetitive tasks, more consistent first drafts, and better documentation of decisions. The key is choosing software that matches how your organization already works: your CRM, workspace, compliance requirements, and budget cycle.

This guide is built for data analysts evaluating software purchases in 2026. We prioritize tools with strong user ratings in the FindStackAI directory, transparent pricing pages, and clear enterprise or team tiers where relevant. Every recommendation below links to a full review with features, pros and cons, pricing, and alternatives so you can validate fit before rolling out to a department.

How we evaluate AI tools for data analysts

Our selection criteria for data analysts include: (1) workflow fit — does the product solve a recurring job, not a one-off demo? (2) Output quality on real tasks in your domain, not cherry-picked prompts. (3) Pricing predictability — free tiers, per-seat costs, usage credits, and overage fees. (4) Integrations with email, CRM, docs, IDE, or creative suites you already pay for. (5) Governance — SSO, admin roles, data retention, and regional availability for regulated teams. (6) Adoption friction — onboarding time, template libraries, and support quality.

We also cross-check alternatives for each tool so you can run a short pilot between two finalists. When a category is crowded — for example chatbots or sales intelligence — we link to dedicated comparison pages (e.g. side-by-side pricing and feature matrices) to shorten procurement research.

Top AI tool recommendations for data analysts

The following 8 tools are our top picks for data analysts based on directory ratings, feature depth, and typical buying patterns. Use the cards above for a quick scan; this section explains when and why each tool earns a place in a modern stack.

Julius AI

As a AI analytics, Julius AI focuses on practical outcomes: ai data analyst chatbot for csv, excel, and databases. Teams evaluating data automation often shortlist Julius AI because it balances accessibility with enough depth for daily professional use. Julius AI lets users upload spreadsheets and ask questions in natural language to get charts, insights, and Python analysis. Analysts and operators use it without writing SQL manually.

Julius AI emphasizes File upload, Auto charts, Python analysis, Sharing as primary building blocks. Rather than optimizing for a single trick, the platform supports multi-step tasks that mirror how professionals actually work: draft, refine, verify, and publish. That structure reduces friction when adopting business intelligence.

Julius AI is commonly used for forecasting support, ad hoc analysis, and metric anomaly review. These scenarios benefit from decision support because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For AI analytics buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

insight generation teams frequently evaluate whether an AI tool reduces operational overhead or simply adds another tab. Julius AI tends to win when there is a clear before/after metric: hours saved, assets produced, or response time improved. Mapping those metrics early helps justify freemium pricing and set realistic expectations for model limitations.

Pricing follows a freemium model (Free-$20/mo). Free or entry tiers are useful for evaluation, while paid plans typically unlock higher limits, faster processing, advanced models, or team controls. Before committing, compare your expected monthly volume against plan caps—especially if multiple teammates share one account. Enterprise buyers should confirm data retention, admin controls, and invoicing options directly with the vendor.

Alternatives such as Obviously AI, ChatGPT, Hex overlap partially with Julius AI. Some prioritize ecosystem lock-in, others emphasize open models or niche quality. If migration cost is low, pilot two options in parallel for a sprint. If migration cost is high—IDE plugins, team templates, brand assets—optimize for long-term workflow fit over small feature gaps.

Julius AI is rated 4.5 out of 5 across 2.800 reviews, indicating broad adoption. For professional use, combine those signals with internal pilots: measure rework rate, factual errors, and time-to-final. That evidence beats generic claims when choosing between competing business intelligence platforms.

Integration tip: pair Julius AI with your existing stack (CRM, IDE, DAM, or docs) instead of isolating it as a standalone toy. decision support value increases when outputs flow into systems your team already checks daily.

For data analysts, Julius AI stands out when low barrier for non-coders; fast exploratory analysis. Trade-offs to plan for: verify sensitive data policies; not a full bi warehouse. Pricing is freemium (Free-$20/mo). Teams often compare Julius AI with Obviously AI and ChatGPT before signing.

• Julius AI full review
• Julius AI alternatives

ChatGPT

As a conversational AI, ChatGPT focuses on practical outcomes: ai assistant for conversation, coding, and creative tasks. Teams evaluating AI chatbot often shortlist ChatGPT because it balances accessibility with enough depth for daily professional use. ChatGPT by OpenAI is the leading AI chatbot for natural conversation, code generation, image analysis, and creative writing. Used by millions for productivity, research, and everyday tasks.

ChatGPT emphasizes Advanced reasoning, Code generation, Image analysis, Web browsing as primary building blocks. Rather than optimizing for a single trick, the platform supports multi-step tasks that mirror how professionals actually work: draft, refine, verify, and publish. That structure reduces friction when adopting virtual assistant.

ChatGPT is commonly used for research and synthesis, customer support drafting, and internal knowledge Q&A. These scenarios benefit from natural language automation because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For conversational AI buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

prompt-based productivity teams frequently evaluate whether an AI tool reduces operational overhead or simply adds another tab. ChatGPT tends to win when there is a clear before/after metric: hours saved, assets produced, or response time improved. Mapping those metrics early helps justify freemium pricing and set realistic expectations for model limitations.

ChatGPT publishes freemium pricing (Free-$20/mo), but effective cost depends on intensity of use. Light individual use may stay on free tiers, while daily professional use usually requires paid access. Compare total cost against alternatives by estimating outputs per month, not just sticker price. Factor in onboarding time and integration effort when calculating ROI.

Buyers often compare ChatGPT with Claude, Perplexity before standardizing. Differences usually appear in output style, integration depth, privacy posture, and pricing mechanics—not raw feature checklists. Run the same three to five real tasks in each candidate tool and score accuracy, edit time, and consistency. Our directory links to dedicated reviews and comparison pages to shorten that evaluation cycle.

Community feedback (4.9/5 from 12.500 reviews) suggests ChatGPT is a credible option in Chatbots. As with any AI chatbot product, quality improves when users provide structured context, examples, and constraints. Maintain a lightweight editorial checklist for anything customer-facing.

Integration tip: pair ChatGPT with your existing stack (CRM, IDE, DAM, or docs) instead of isolating it as a standalone toy. natural language automation value increases when outputs flow into systems your team already checks daily.

For data analysts, ChatGPT stands out when industry-leading quality; easy to use. Trade-offs to plan for: premium features require subscription; requires internet connection. Pricing is freemium (Free-$20/mo). Teams often compare ChatGPT with Claude and Perplexity before signing.

• ChatGPT full review
• ChatGPT alternatives

Claude

Claude is a conversational AI platform designed to help individuals and teams work faster with prompt-based productivity. Advanced AI assistant by Anthropic with strong reasoning The product fits into modern AI tool stacks where speed, clarity, and repeatable output matter more than manual busywork. Claude by Anthropic excels at long-context analysis, safe responses, and detailed writing. Popular with professionals who need thoughtful, nuanced AI assistance.

The feature set—including 200K context window, Document analysis, Code assistance, Safe outputs—is designed for iterative work. Most teams start with a narrow use case, validate output quality, then expand into adjacent tasks like summarization, transformation, or generation. This progression mirrors how other conversational AI products become embedded in daily operations.

Claude is commonly used for customer support drafting, coding and debugging assistance, and internal knowledge Q&A. These scenarios benefit from natural language automation because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For conversational AI buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

Where Claude shines in automation is repeatable micro-workflows—tasks that take five to twenty minutes manually but add up across a week. Examples include batch edits, structured summaries, and variant generation. Combined with AI chatbot, these micro-workflows compound into meaningful productivity gains without requiring custom engineering.

Pricing follows a freemium model (Free-$20/mo). Free or entry tiers are useful for evaluation, while paid plans typically unlock higher limits, faster processing, advanced models, or team controls. Before committing, compare your expected monthly volume against plan caps—especially if multiple teammates share one account. Enterprise buyers should confirm data retention, admin controls, and invoicing options directly with the vendor.

Alternatives such as ChatGPT, Perplexity overlap partially with Claude. Some prioritize ecosystem lock-in, others emphasize open models or niche quality. If migration cost is low, pilot two options in parallel for a sprint. If migration cost is high—IDE plugins, team templates, brand assets—optimize for long-term workflow fit over small feature gaps.

Claude is rated 4.8 out of 5 across 8.900 reviews, indicating broad adoption. For professional use, combine those signals with internal pilots: measure rework rate, factual errors, and time-to-final. That evidence beats generic claims when choosing between competing virtual assistant platforms.

Security note: review data handling, retention, and training policies before uploading sensitive material. Many AI chatbot tools offer business tiers with stronger controls—worth evaluating if you operate in regulated industries.

For data analysts, Claude stands out when excellent reasoning; strong safety focus. Trade-offs to plan for: fewer integrations than chatgpt; free tier has usage limits. Pricing is freemium (Free-$20/mo). Teams often compare Claude with ChatGPT and Perplexity before signing.

• Claude full review
• Claude alternatives

Hex

As a AI analytics, Hex focuses on practical outcomes: collaborative data notebook with ai sql and python assist. Teams evaluating data automation often shortlist Hex because it balances accessibility with enough depth for daily professional use. Hex combines SQL, Python, and visualizations in shareable notebooks with AI assistance for queries and charts. Data teams replace siloed Jupyter and BI exports with collaborative analysis.

Hex emphasizes SQL + Python, AI assist, Version control, Embedded apps as primary building blocks. Rather than optimizing for a single trick, the platform supports multi-step tasks that mirror how professionals actually work: draft, refine, verify, and publish. That structure reduces friction when adopting business intelligence.

Hex is commonly used for executive reporting, ad hoc analysis, and metric anomaly review. These scenarios benefit from decision support because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For AI analytics buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

insight generation teams frequently evaluate whether an AI tool reduces operational overhead or simply adds another tab. Hex tends to win when there is a clear before/after metric: hours saved, assets produced, or response time improved. Mapping those metrics early helps justify freemium pricing and set realistic expectations for model limitations.

Pricing follows a freemium model (Free-$36/editor/mo). Free or entry tiers are useful for evaluation, while paid plans typically unlock higher limits, faster processing, advanced models, or team controls. Before committing, compare your expected monthly volume against plan caps—especially if multiple teammates share one account. Enterprise buyers should confirm data retention, admin controls, and invoicing options directly with the vendor.

Alternatives such as Julius AI, Mode, Observable overlap partially with Hex. Some prioritize ecosystem lock-in, others emphasize open models or niche quality. If migration cost is low, pilot two options in parallel for a sprint. If migration cost is high—IDE plugins, team templates, brand assets—optimize for long-term workflow fit over small feature gaps.

Hex is rated 4.6 out of 5 across 2.400 reviews, indicating broad adoption. For professional use, combine those signals with internal pilots: measure rework rate, factual errors, and time-to-final. That evidence beats generic claims when choosing between competing business intelligence platforms.

Integration tip: pair Hex with your existing stack (CRM, IDE, DAM, or docs) instead of isolating it as a standalone toy. decision support value increases when outputs flow into systems your team already checks daily.

For data analysts, Hex stands out when modern data notebook ux; great collaboration. Trade-offs to plan for: pricing scales with editors; not a warehouse replacement. Pricing is freemium (Free-$36/editor/mo). Teams often compare Hex with Julius AI and Mode before signing.

• Hex full review
• Hex alternatives

Power BI Copilot

Power BI Copilot is a AI analytics platform designed to help individuals and teams work faster with insight generation. Microsoft Copilot for Power BI reports and DAX The product fits into modern AI tool stacks where speed, clarity, and repeatable output matter more than manual busywork. Power BI Copilot helps authors build reports, write DAX, and summarize insights using natural language inside Microsoft Power BI. It targets organizations on Microsoft 365 and Fabric.

The feature set—including Report summaries, DAX assist, Natural language Q&A, Fabric integration—is designed for iterative work. Most teams start with a narrow use case, validate output quality, then expand into adjacent tasks like summarization, transformation, or generation. This progression mirrors how other AI analytics products become embedded in daily operations.

Power BI Copilot is commonly used for ad hoc analysis, executive reporting, and metric anomaly review. These scenarios benefit from decision support because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For AI analytics buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

Where Power BI Copilot shines in automation is repeatable micro-workflows—tasks that take five to twenty minutes manually but add up across a week. Examples include batch edits, structured summaries, and variant generation. Combined with data automation, these micro-workflows compound into meaningful productivity gains without requiring custom engineering.

Pricing follows a paid model (Pro + Copilot license). Free or entry tiers are useful for evaluation, while paid plans typically unlock higher limits, faster processing, advanced models, or team controls. Before committing, compare your expected monthly volume against plan caps—especially if multiple teammates share one account. Enterprise buyers should confirm data retention, admin controls, and invoicing options directly with the vendor.

Alternatives such as Tableau AI, Looker, ThoughtSpot overlap partially with Power BI Copilot. Some prioritize ecosystem lock-in, others emphasize open models or niche quality. If migration cost is low, pilot two options in parallel for a sprint. If migration cost is high—IDE plugins, team templates, brand assets—optimize for long-term workflow fit over small feature gaps.

Power BI Copilot is rated 4.5 out of 5 across 3.200 reviews, indicating broad adoption. For professional use, combine those signals with internal pilots: measure rework rate, factual errors, and time-to-final. That evidence beats generic claims when choosing between competing business intelligence platforms.

Security note: review data handling, retention, and training policies before uploading sensitive material. Many data automation tools offer business tiers with stronger controls—worth evaluating if you operate in regulated industries.

For data analysts, Power BI Copilot stands out when deep microsoft stack fit; enterprise governance. Trade-offs to plan for: requires power bi licensing; copilot add-on cost. Pricing is paid (Pro + Copilot license). Teams often compare Power BI Copilot with Tableau AI and Looker before signing.

• Power BI Copilot full review
• Power BI Copilot alternatives

Tableau AI

As a AI analytics, Tableau AI focuses on practical outcomes: salesforce tableau with einstein ai for analytics. Teams evaluating data automation often shortlist Tableau AI because it balances accessibility with enough depth for daily professional use. Tableau AI (Einstein) adds automated insights, natural language queries, and predictive features to Tableau dashboards. Enterprise analytics teams use it within Salesforce Customer 360.

Tableau AI emphasizes Pulse insights, NLQ, Predictive models, Salesforce tie-in as primary building blocks. Rather than optimizing for a single trick, the platform supports multi-step tasks that mirror how professionals actually work: draft, refine, verify, and publish. That structure reduces friction when adopting business intelligence.

Tableau AI is commonly used for metric anomaly review, ad hoc analysis, and forecasting support. These scenarios benefit from decision support because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For AI analytics buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

insight generation teams frequently evaluate whether an AI tool reduces operational overhead or simply adds another tab. Tableau AI tends to win when there is a clear before/after metric: hours saved, assets produced, or response time improved. Mapping those metrics early helps justify paid pricing and set realistic expectations for model limitations.

Pricing follows a paid model (Enterprise pricing). Free or entry tiers are useful for evaluation, while paid plans typically unlock higher limits, faster processing, advanced models, or team controls. Before committing, compare your expected monthly volume against plan caps—especially if multiple teammates share one account. Enterprise buyers should confirm data retention, admin controls, and invoicing options directly with the vendor.

Alternatives such as Power BI Copilot, Looker, Qlik overlap partially with Tableau AI. Some prioritize ecosystem lock-in, others emphasize open models or niche quality. If migration cost is low, pilot two options in parallel for a sprint. If migration cost is high—IDE plugins, team templates, brand assets—optimize for long-term workflow fit over small feature gaps.

Tableau AI is rated 4.5 out of 5 across 2.400 reviews, indicating broad adoption. For professional use, combine those signals with internal pilots: measure rework rate, factual errors, and time-to-final. That evidence beats generic claims when choosing between competing business intelligence platforms.

Integration tip: pair Tableau AI with your existing stack (CRM, IDE, DAM, or docs) instead of isolating it as a standalone toy. decision support value increases when outputs flow into systems your team already checks daily.

For data analysts, Tableau AI stands out when gold standard viz; enterprise trust. Trade-offs to plan for: expensive for small teams; salesforce ecosystem bias. Pricing is paid (Enterprise pricing). Teams often compare Tableau AI with Power BI Copilot and Looker before signing.

• Tableau AI full review
• Tableau AI alternatives

Obviously AI

If you need decision support without rebuilding your entire stack, Obviously AI offers a focused AI analytics experience. No-code predictive analytics for business teams It is commonly compared with alternatives in the same category when buyers prioritize reliability, pricing flexibility, and ease of adoption. Obviously AI lets analysts upload spreadsheets and build predictive models in minutes without SQL or Python. It explains predictions in plain language for business users.

Core capabilities center on AutoML, Plain English insights, API, Integrations. In practice, users chain these features into repeatable workflows instead of treating each session as a blank slate. That workflow mindset is where data automation delivers the most value, especially when prompts, templates, or integrations are reused across projects.

Obviously AI is commonly used for metric anomaly review, forecasting support, and executive reporting. These scenarios benefit from decision support because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For AI analytics buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

Automation value comes from reducing context switching. Instead of exporting text, images, or code into multiple apps, Obviously AI keeps more of the loop inside one interface. That matters for business intelligence where handoffs between tools create delays and quality drift. When integrated thoughtfully, it supports lightweight automation: templated prompts, reusable assets, and predictable review stages.

On pricing, Obviously AI is positioned as paid with From $75/mo. Most users start on a limited tier, measure usage for two to four weeks, then upgrade if bottlenecks appear. Watch for per-seat costs, credit systems, and overage rules. If you rely on Obviously AI in production workflows, budget for paid access rather than assuming free limits will remain sufficient.

When Obviously AI is not the right fit, teams typically pivot to DataRobot, Pecan. Common reasons include regional availability, compliance requirements, model preference, or UI familiarity. Treat alternatives as substitutes for specific jobs-to-be-done rather than perfect clones; the best choice depends on which trade-offs your team accepts.

With a 4.2/5 average from 600 reviews, Obviously AI has established a substantial user base. Ratings reflect real-world satisfaction across ease of use, output quality, and support—not lab benchmarks alone. New users should still validate on their own datasets, languages, and domains because AI analytics performance varies by task complexity.

Implementation tip: document three "golden prompts" or workflows your team trusts, then iterate from that baseline. This reduces prompt drift and makes onboarding easier for new teammates exploring AI analytics.

For data analysts, Obviously AI stands out when very approachable; quick predictions. Trade-offs to plan for: not for deep ml research; dataset size limits. Pricing is paid (From $75/mo). Teams often compare Obviously AI with DataRobot and Pecan before signing.

• Obviously AI full review
• Obviously AI alternatives

Pecan AI

Pecan AI is a AI analytics platform designed to help individuals and teams work faster with insight generation. Predictive analytics platform for SQL-ready data teams The product fits into modern AI tool stacks where speed, clarity, and repeatable output matter more than manual busywork. Pecan AI automates feature engineering and model building on warehouse data for churn, LTV, and demand forecasting. Data teams deploy predictions back to BI tools.

The feature set—including SQL integration, AutoML, Churn models, Monitoring—is designed for iterative work. Most teams start with a narrow use case, validate output quality, then expand into adjacent tasks like summarization, transformation, or generation. This progression mirrors how other AI analytics products become embedded in daily operations.

Pecan AI is commonly used for metric anomaly review, executive reporting, and dashboard interpretation. These scenarios benefit from decision support because they require both speed and consistency. Users who treat the tool as a co-pilot—providing context, examples, and constraints—typically see better results than one-line prompts copied from generic templates. For AI analytics buyers, the strongest fit is often teams that repeat similar tasks weekly and can standardize prompts, checklists, or approval steps around the output.

Where Pecan AI shines in automation is repeatable micro-workflows—tasks that take five to twenty minutes manually but add up across a week. Examples include batch edits, structured summaries, and variant generation. Combined with data automation, these micro-workflows compound into meaningful productivity gains without requiring custom engineering.

Pricing follows a contact model (custom enterprise pricing). Free or entry tiers are useful for evaluation, while paid plans typically unlock higher limits, faster processing, advanced models, or team controls. Before committing, compare your expected monthly volume against plan caps—especially if multiple teammates share one account. Enterprise buyers should confirm data retention, admin controls, and invoicing options directly with the vendor.

Alternatives such as DataRobot, Obviously AI overlap partially with Pecan AI. Some prioritize ecosystem lock-in, others emphasize open models or niche quality. If migration cost is low, pilot two options in parallel for a sprint. If migration cost is high—IDE plugins, team templates, brand assets—optimize for long-term workflow fit over small feature gaps.

Pecan AI is rated 4.3 out of 5 across 500 reviews, indicating broad adoption. For professional use, combine those signals with internal pilots: measure rework rate, factual errors, and time-to-final. That evidence beats generic claims when choosing between competing business intelligence platforms.

Security note: review data handling, retention, and training policies before uploading sensitive material. Many data automation tools offer business tiers with stronger controls—worth evaluating if you operate in regulated industries.

For data analysts, Pecan AI stands out when warehouse native; business-friendly outputs. Trade-offs to plan for: enterprise sales cycle; needs clean data pipelines. Pricing is contact (see official site). Teams often compare Pecan AI with DataRobot and Obviously AI before signing.

• Pecan AI full review
• Pecan AI alternatives

Building a practical AI stack for data analysts

Most data analysts do not need fifteen subscriptions. A durable pattern is three layers: (1) a general assistant for drafting and Q&A — often ChatGPT, Claude, or Perplexity; (2) a domain-specific tool tied to your core workflow (CRM, IDE, design suite, support desk, or SEO platform); (3) an automation or knowledge layer — Zapier, Glean, Notion AI, or similar — to move outputs into systems of record. Add specialists (voice, video, enrichment) only when a role owns that output weekly.

Run a 30-day pilot with five volunteers across functions. Give them a shared prompt library and measure time saved on three recurring tasks — not vanity usage stats. Kill tools that do not clear a measurable bar; consolidate spend on winners. Review quarterly as vendors ship new models and pricing changes.

Pricing, procurement, and ROI

AI software pricing in 2026 still clusters into free/freemium, per-seat SaaS, usage credits, and enterprise contracts. For data analysts, model total cost as: seats × price + expected overage + onboarding time. Negotiate annual deals when daily active users exceed 60% of licensed seats. Ask vendors about training data policies, SOC 2, and API rate limits before procurement signs.

ROI is easiest to defend when tied to revenue or hours saved: faster campaign launches, shorter sales cycles, fewer support escalations, or reduced agency spend. Document a baseline before rollout so finance can compare quarter-over-quarter.

Security, privacy, and governance

data analysts handling customer data, financials, or IP should default to vendors with clear data processing terms, optional zero-retention modes, and SSO. Avoid pasting regulated data into consumer chat tiers without legal review. Segment tools: approved for confidential work vs drafting only. Train teams on verification — AI outputs can be fluent and wrong.

Compare tools before you buy

Use our comparison hub for side-by-side reviews of popular pairs, or open category hubs: business intelligence, chatbots. Featured tools on this page: Julius AI, ChatGPT, Claude, Hex, Power BI Copilot, Tableau AI, Obviously AI, Pecan AI.